Abstract
Automatic prediction on the peer-review aspect scores of academic papers can be a useful assistant tool for both reviewers and authors. To handle the small size of published datasets on the target aspect of scores, we propose a multi-task approach to leverage additional information from other aspects of scores for improving the performance of the target. Because one of the problems of building multi-task models is how to select the proper resources of auxiliary tasks and how to select the proper shared structures. We propose a multi-task shared structure encoding approach which automatically selects good shared network structures as well as good auxiliary resources. The experiments based on peer-review datasets show that our approach is effective and has better performance on the target scores than the single-task method and naive multi-task methods.- Anthology ID:
- 2020.sdp-1.14
- Volume:
- Proceedings of the First Workshop on Scholarly Document Processing
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- sdp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 121–126
- Language:
- URL:
- https://aclanthology.org/2020.sdp-1.14
- DOI:
- 10.18653/v1/2020.sdp-1.14
- Cite (ACL):
- Jiyi Li, Ayaka Sato, Kazuya Shimura, and Fumiyo Fukumoto. 2020. Multi-task Peer-Review Score Prediction. In Proceedings of the First Workshop on Scholarly Document Processing, pages 121–126, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multi-task Peer-Review Score Prediction (Li et al., sdp 2020)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2020.sdp-1.14.pdf
- Data
- PeerRead